Relationship between LiDAR-derived forest canopy height and Landsat images

The mean and standard deviation (SD) of light detection and ranging (LiDAR)-derived canopy height are related to forest structure. However, LiDAR data typically cover a limited area and have a high economic cost compared with satellite optical imagery. Optical images may be required to extrapolate LiDAR height measurements across a broad landscape. Different spectral indices were obtained from three Landsat scenes. The mean, median, SD and coefficient of variation (CV) of LiDAR canopy height measurements were calculated in 30-m square blocks corresponding with Landsat Enhanced Thematic Mapper Plus (ETM+) pixels. Correlation and forward stepwise regression analysis was applied to these data sets. Mean and median LiDAR height versus normalized difference vegetation index (NDVI), normalized difference moisture index (NDMI), normalized burn ratio (NBR) and wetness Tasseled Cap showed the best correlation coefficients (R 2 ranging between −0.62 and −0.76). Nineteen regression models were obtained (R 2 = 0.65–0.70). These results show that LiDAR-derived canopy height may be associated with Landsat spectral indices. This approach is of interest in sustainable forest management, although further research is required to improve accuracy.

[1]  Simone R. Freitas,et al.  Relationships between forest structure and vegetation indices in Atlantic Rainforest , 2005 .

[2]  M. Steininger.  Satellite estimation of tropical secondary forest above-ground biomass: Data from Brazil and Bolivia , 2000 .

[3]  Ruiliang Pu,et al.  Coniferous forest leaf area index estimation along the Oregon transect using compact airborne spectrographic imager data , 1995 .

[4]  Dengsheng Lu,et al.  Relationships between forest stand parameters and Landsat TM spectral responses in the Brazilian Amazon Basin , 2004 .

[5]  Yvan Pannatier,et al.  Variowin: Software for Spatial Data Analysis in 2D , 1996 .

[6]  Joanne C. White,et al.  Integrating profiling LIDAR with Landsat data for regional boreal forest canopy attribute estimation and change characterization , 2007 .

[7]  J. Chen,et al.  Retrieving Leaf Area Index of Boreal Conifer Forests Using Landsat TM Images , 1996 .

[8]  D. Roberts,et al.  Small-footprint lidar estimation of sub-canopy elevation and tree height in a tropical rain forest landscape , 2004 .

[9]  Patrick D. Gerard,et al.  Characterizing vertical forest structure using small-footprint airborne LiDAR , 2003 .

[10]  S. Popescu,et al.  Seeing the Trees in the Forest: Using Lidar and Multispectral Data Fusion with Local Filtering and Variable Window Size for Estimating Tree Height , 2004 .

[11]  D. Donoghue,et al.  Using LiDAR to compare forest height estimates from IKONOS and Landsat ETM+ data in Sitka spruce plantation forests , 2006 .

[12]  Valérie Demarez,et al.  Seasonal variation of leaf chlorophyll content of a temperate forest. Inversion of the PROSPECT model , 1999 .

[13]  W. Cohen,et al.  Estimating structural attributes of Douglas-fir/western hemlock forest stands from Landsat and SPOT imagery , 1992 .

[14]  J. Hyyppä,et al.  Review of methods of small‐footprint airborne laser scanning for extracting forest inventory data in boreal forests , 2008 .

[15]  Xuexia Chen,et al.  Using lidar and effective LAI data to evaluate IKONOS and Landsat 7 ETM+ vegetation cover estimates in a ponderosa pine forest , 2004 .

[16]  W. Cohen,et al.  Lidar Remote Sensing for Ecosystem Studies , 2002 .

[17]  G. Quinn,et al.  Experimental Design and Data Analysis for Biologists , 2002 .

[18]  W. Cohen,et al.  An improved strategy for regression of biophysical variables and Landsat ETM+ data. , 2003 .

[19]  W. Cohen,et al.  Integration of lidar and Landsat ETM+ data for estimating and mapping forest canopy height , 2002 .

[20]  W. Cohen,et al.  An evaluation of alternate remote sensing products for forest inventory, monitoring, and mapping of Douglas-fir forests in western Oregon , 2001 .

[21]  G. A. Blackburn,et al.  Mapping individual tree location, height and species in broadleaved deciduous forest using airborne LIDAR and multi‐spectral remotely sensed data , 2005 .

[22]  Warren B. Cohen,et al.  Object-based semi-automatic approach for forest structure characterization using lidar data in heterogeneous Pinus sylvestris stands. , 2008 .

[23]  Limin Yang,et al.  Derivation of a tasselled cap transformation based on Landsat 7 at-satellite reflectance , 2002 .

[24]  M. Wulder,et al.  Forest inventory height update through the integration of lidar data with segmented Landsat imagery , 2003 .

[25]  Karl Fred Huemmrich,et al.  Remote Sensing of Forest Biophysical Structure Using Mixture Decomposition and Geometric Reflectance Models , 1995 .

[26]  J. Swenson,et al.  A comparison of lidar, radar, and field measurements of canopy height in pine and hardwood forests of southeastern North America , 2009 .

[27]  P. Chavez.  Image-Based Atmospheric Corrections - Revisited and Improved , 1996 .

[28]  W. Cohen,et al.  Geographic variability in lidar predictions of forest stand structure in the Pacific Northwest , 2005 .

[29]  W. Cohen,et al.  Estimating the age and structure of forests in a multi-ownership landscape of western Oregon, U.S.A. , 1995 .

[30]  Mark E. Jakubauskas,et al.  Canonical correlation analysis of coniferous forest spectral and biotic relations , 1996 .

[31]  Philip Lewis,et al.  Investigation of the Utility of Spectral Vegetation Indices for Determining Information on Coniferous Forests , 1998 .

[32]  Steven E. Franklin,et al.  Estimation of forest Leaf Area Index using remote sensing and GIS data for modelling net primary production , 1997 .

[33]  W. Keeton,et al.  Disturbances and structural development of natural forest ecosystems with silvicultural implications, using Douglas-fir forests as an example , 2002 .

[34]  S. Ustin,et al.  Multi-temporal vegetation canopy water content retrieval and interpretation using artificial neural networks for the continental USA , 2008 .

[35]  Eric P. Crist,et al.  A Physically-Based Transformation of Thematic Mapper Data---The TM Tasseled Cap , 1984, IEEE Transactions on Geoscience and Remote Sensing.

[36]  W. Cohen,et al.  Patterns of covariance between forest stand and canopy structure in the Pacific Northwest , 2005 .